用自回归Transformer模拟高能物理喷注辐射，可外推到更高粒子数。
Extrapolating Jet Radiation with Autoregressive Transformers
- 用自回归Transformer建模喷注辐射的分步生成过程。
- 在训练数据基础上外推至更高粒子数（>100），准确率仍保持。
- 适合高能物理模拟、粒子探测器设计等研究者使用。
生成网络是快速模拟大型强子对撞机事件的有力工具。自回归Transformer能够生成粒子数量可变的事件，非常契合量子色动力学喷注辐射的物理特性，并具备推广到更高粒子多重性的潜力。我们展示如何让Transformer学习喷注辐射的分解似然函数，并实现粒子数的外推。为此，可通过重采样训练数据及修改似然损失函数来提升性能。
原文摘要 · Abstract (English)
Generative networks are an exciting tool for fast LHC event fixed number of particles. Autoregressive transformers allow us to generate events containing variable numbers of particles, very much in line with the physics of QCD jet radiation, and offer the possibility to generalize to higher multiplicities. We show how transformers can learn a factorized likelihood for jet radiation and extrapolate in terms of the number of generated jets. For this extrapolation, bootstrapping training data and training with modifications of the likelihood loss can be used.
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